- Developers & Engineers: Deploy reasoning-capable LLMs without MoE complexity—ideal for edge and server deployment.
- Researchers & Students: Study distilled reasoning behavior in smaller models with full transparency.
- Enterprises & Startups: Benefit from top-tier benchmark performance with reduced compute footprints.
- Open-Source Advocates: Use, modify, and redistribute models freely under MIT license.
- Benchmarkers: Compare distilled versions (1.5B–70B) with dense peers like Qwen and Llama in reasoning and code tasks.
How to Use DeepSeek R1 Distill?
- Choose a Variant: Options include Qwen-1.5B, 7B, 14B, 32B, and Llama-8B/70B distilled checkpoints.
- Load Locally or on Cloud: Install via Hugging Face or run with tools like Ollama or vLLM.
- Provide Prompts: Use up to 32K tokens; models support chain-of-thought reasoning in math, coding, QA.
- Run Inference: Serve via local or cloud inference frameworks, or deploy as Chatbots or API services.
- Optimize & Expand: Best scaling seen in Qwen-32B variant; custom fine-tuning possible.
Qwen‑32B leads dense model benchmarks across tasks
Fully open-source—great for innovation and transparency
Versatile sizes for diverse deployment targets
Easy deployment via Hugging Face, Ollama, vLLM
- - *High Benchmark Efficiency*: Distilled variants, especially Qwen-32B, rival or surpass o1-mini on math, code, and reasoning tests.
- - *Compact Reasoning Models*: Bring chain-of-thought from massive MoE models into manageable dense models.
- - *Open-Source & MIT Licensed*: Enables commercial and academic use, fine-tuning, redistribution.
- - *Accessibility*: Usable on commodity hardware with inference tools like Ollama, vLLM.
- - *Broad Range & Flexibility*: Available in sizes from 1.5B to 70B to fit different latency/accuracy needs.
- Still lacks MoE-level context window and multimodal input
- Slightly behind full DeepSeek R1 in reasoning depth
- Early versions may show benchmark volatility depending on evaluation setup
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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